This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models.

Accurately predicting the peak height of the F2 layer (hmF₂) is crucial for radio communications, satellite navigation, and space weather studies, yet traditional empirical models often lack precision. To address this, we developed a global hmF₂ prediction model using a Multilayer Perceptron (MLP) neural network, based on COSMIC radio occultation observations from 2007 to 2023. Evaluated on independent test sets from 2014 and 2019, the MLP model achieved correlation coefficients of 0.877 and 0.853 with root mean square errors of 22.3 km and 19.1 km, respectively, significantly outperforming the machine learning approaches like XGBoost and Transformer. The model also demonstrated strong generalization on an independent validation set constructed from 2014 and 2019 GIRO data, with a correlation of 0.785 and RMSE of 26.1 km, surpassing NPHM, and XGBoost. SHAP interpretability analysis identified geographic latitude, cosine of latitude, solar F10.7 index, and annual/daily harmonic terms as the most influential physical features. Error analysis showed a mean prediction error of −3.2 km and a standard deviation of 21.8 km, with stable performance during quiet periods and larger errors primarily during disturbed conditions. This study provides a reliable tool for high-accuracy hmF₂ prediction and enhances the understanding of the physical mechanisms controlling its variability.